ANN model for predicting acrylonitrile wastewater degradation in supercritical water oxidation

Abstract

The discharged acrylonitrile wastewater had aroused more and more attention due to the increasingly serious water pollution. Supercritical water oxidation (SCWO) was an effective and fast way to degrade it completely without secondary pollution. To better illustrate the performances of SCWO of acrylonitrile wastewater, the experimental research covered the effects of different operation conditions on TOC reduction, such as reduced temperature (T/Tc), reduced pressure (P/Pc), initial total organic carbon concentration (TOC0), stoichiometric ratio (SR) and residence time (t). For a more accurate prediction of the emissions, two kinds of artificial neural network (ANN) models were adopt to simulate the TOC reductions in the processes of SCWO of acrylonitrile wastewater, including the Cascade-forward back propagation neural network (CFBPNN) and Feed-forward back propagation neural network (FFBPNN). The input parameters of ANN models were T/Tc, P/Pc, TOC0, SR and t. The output parameter was TOC reduction (η). The mean square error (E2) and the coefficient of determination (R2) were used to evaluate the model performances, respectively. Both the model and the experiment results had shown the TOC reduction could be greatly improved by reduced temperature, reduced pressure, initial TOC concentration, stoichiometric ratio and residence time. The FFBPNN model with the hidden neurons numbers of 12 was shown much better performances than the CFBPNN model.